Abstract: In accordance with an embodiment, described herein is a system and method for automatically enriching datasets in a data analytics environment, with system knowledge data. The system can operate, upon an analysis of a data set, to automatically enrich the data set. Users of data analytics environments, such as business users preparing data visualizations, may be unaware of additional data and system knowledge data that could be utilized to improve the data visualizations. The systems and methods described herein can provide an automatic enrichment of data from, for example, a knowledge repository, which can be delivered to a data analytics customer using various delivery means.

Abstract: In accordance with an embodiment, described herein is a system and method for providing a network-visualization browser extension in an analytics environment. An analytic applications environment can be provided by, or otherwise operate at, a computer system providing access to a data warehouse, or data warehouse instance. The system is adapted for generating a network graph from analytic artifacts, including display of one or more dataflows as dataflow constellation visualizations that illustrates the dataflows and a lineage or other relationship information. The systems and methods disclosed herein can provide tools to provide insights for users of an analytics environment with regard to the users' analytic artifacts and relationships among the same.

Abstract: A computer-implemented method includes providing a fixed portion of a data model, uploading external files to an extended portion of the data model, matching at least some parts of the external files to the fixed portions of the data model, and using data from both the fixed and extended portions of the data model together. A computer-implemented method includes receiving a query from a user to a data model that includes a stored subject area portion and a stored extended subject area portion, accessing at least one storage to retrieve the subject area portion and the extended subject area portion, joining relevant portions of the subject area and extended subject area, extracting answers to the query from the joined portions, and reporting the answers to the user.

Abstract: Described herein are systems and methods for generating automatic insights of analytics data. Generally, when data is uploaded or otherwise linked to or made accessible to an analytics environment, a skilled user is needed in order to generate meaningful data visualizations. The systems and methods described herein provide an automatic mechanism to generate for viewing and selection a set of meaningful data visualizations, wherein said generation is based upon a determined set of metrics, scored data columns, and scored visualizations.

Abstract: In accordance with an embodiment, described herein is a system and method for use of text analytics to transform, analyze, and visualize data, including support for data flows of unstructured text or other types of textual data input. Additionally described are various examples of algorithmic processes and user interfaces that can be used to enable text analytics in particular environments or use cases. In accordance with an embodiment, the system can be implemented within a cloud environment that enables self-service text analytics. A user, for example an organizational business user who may not be expert in the use of machine learning as applied to data processing, can interact with the system via a user interface, to apply natural language processing or other text analysis techniques to a data flow or set of input data, to generate visualizations or other types of useful information associated with the data.

Abstract: A computer-implemented method includes providing a fixed portion of a data model, uploading external files to an extended portion of the data model, matching at least some parts of the external files to the fixed portions of the data model, and using data from both the fixed and extended portions of the data model together. A computer-implemented method includes receiving a query from a user to a data model that includes a stored subject area portion and a stored extended subject area portion, accessing at least one storage to retrieve the subject area portion and the extended subject area portion, joining relevant portions of the subject area and extended subject area, extracting answers to the query from the joined portions, and reporting the answers to the user.

Abstract: A computer-implemented method includes providing a fixed portion of a data model, uploading external files to an extended portion of the data model, matching at least some parts of the external files to the fixed portions of the data model, and using data from both the fixed and extended portions of the data model together. A computer-implemented method includes receiving a query from a user to a data model that includes a stored subject area portion and a stored extended subject area portion, accessing at least one storage to retrieve the subject area portion and the extended subject area portion, joining relevant portions of the subject area and extended subject area, extracting answers to the query from the joined portions, and reporting the answers to the user.

Abstract: A computer-implemented method includes providing a fixed portion of a data model, uploading external files to an extended portion of the data model, matching at least some parts of the external files to the fixed portions of the data model, and using data from both the fixed and extended portions of the data model together. A computer-implemented method includes receiving a query from a user to a data model that includes a stored subject area portion and a stored extended subject area portion, accessing at least one storage to retrieve the subject area portion and the extended subject area portion, joining relevant portions of the subject area and extended subject area, extracting answers to the query from the joined portions, and reporting the answers to the user.